Reinforcement learning with industrial continuous process
RL is a generic technique that can be applied to any MDP system. From the looks of it you have data to produce a state-space model of your system (system identification, excluding your existing control loops) and then you can use that to drive a simulated exploration of your process and discover a control policy. As this is a continuous process, some discretization will be required

so yes, your quest is feasible, but not trivial! 

I experimented sometime ago with the cartpole balancing system, which is a toy, but I found that a good old PID was much better than AI :)